Hybrid Resource Quota Scaling for Kubernetes-Based Edge Computing Systems

In the Kubernetes edge computing environment, Resource Quota plays a vital role in efficient limited resource management because it defines the maximum resources that each service or tenant can use. Therefore, when edge nodes serve multiple services simultaneously, resource quota prevents any single service from monopolizing resources. However, the manual resource quota configuration mechanism in current Kubernetes-based management platforms is not dynamic enough to handle fluctuating resource demands of services over time. Slow quota extension during surge traffic prevents scaling up necessary pods and degrades service performance, while over-allocating quotas during light traffic might occupy valuable resources that other services may need. This study proposes a Dynamic Resource Quota Auto-scaling Framework, combining proactive scaling based on workload predictions with reactive mechanisms to handle both inaccurate predictions and unforeseeable events. This framework not only optimizes resource allocation but also maintains stable performance, reduces deployment failures, and prevents over-allocation during scaling in high-demand periods.